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Add zh_ Langchain into dependent files

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This commit is contained in:
kainstan
2023-06-06 09:37:04 +08:00
parent 0a83ba91e9
commit 6d7ee17dbd
2 changed files with 111 additions and 93 deletions
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201
crazy_functions/crazy_utils.py
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@ -6,12 +6,14 @@ def input_clipping(inputs, history, max_token_limit):
import numpy as np
from request_llm.bridge_all import model_info
enc = model_info["gpt-3.5-turbo"]['tokenizer']
def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
def get_token_num(txt):
return len(enc.encode(txt, disallowed_special=()))
mode = 'input-and-history'
# 当 输入部分的token占比 小于 全文的一半时，只裁剪历史
input_token_num = get_token_num(inputs)
if input_token_num < max_token_limit//2:
if input_token_num < max_token_limit // 2:
mode = 'only-history'
max_token_limit = max_token_limit - input_token_num
@ -19,13 +21,13 @@ def input_clipping(inputs, history, max_token_limit):
everything.extend(history)
n_token = get_token_num('\n'.join(everything))
everything_token = [get_token_num(e) for e in everything]
delta = max(everything_token) // 16 # 截断时的颗粒度
delta = max(everything_token) // 16 # 截断时的颗粒度
while n_token > max_token_limit:
where = np.argmax(everything_token)
encoded = enc.encode(everything[where], disallowed_special=())
clipped_encoded = encoded[:len(encoded)-delta]
everything[where] = enc.decode(clipped_encoded)[:-1] # -1 to remove the may-be illegal char
clipped_encoded = encoded[:len(encoded) - delta]
everything[where] = enc.decode(clipped_encoded)[:-1] # -1 to remove the may-be illegal char
everything_token[where] = get_token_num(everything[where])
n_token = get_token_num('\n'.join(everything))
@ -38,11 +40,11 @@ def input_clipping(inputs, history, max_token_limit):
def request_gpt_model_in_new_thread_with_ui_alive(
inputs, inputs_show_user, llm_kwargs,
inputs, inputs_show_user, llm_kwargs,
chatbot, history, sys_prompt, refresh_interval=0.2,
handle_token_exceed=True,
handle_token_exceed=True,
retry_times_at_unknown_error=2,
):
):
"""
Request GPT model，请求GPT模型同时维持用户界面活跃。
@ -75,7 +77,7 @@ def request_gpt_model_in_new_thread_with_ui_alive(
exceeded_cnt = 0
while True:
# watchdog error
if len(mutable) >= 2 and (time.time()-mutable[1]) > 5:
if len(mutable) >= 2 and (time.time() - mutable[1]) > 5:
raise RuntimeError("检测到程序终止。")
try:
# 【第一种情况】：顺利完成
@ -92,14 +94,14 @@ def request_gpt_model_in_new_thread_with_ui_alive(
p_ratio, n_exceed = get_reduce_token_percent(str(token_exceeded_error))
MAX_TOKEN = 4096
EXCEED_ALLO = 512 + 512 * exceeded_cnt
inputs, history = input_clipping(inputs, history, max_token_limit=MAX_TOKEN-EXCEED_ALLO)
inputs, history = input_clipping(inputs, history, max_token_limit=MAX_TOKEN - EXCEED_ALLO)
mutable[0] += f'[Local Message] 警告，文本过长将进行截断，Token溢出数：{n_exceed}。\n\n'
continue # 返回重试
continue # 返回重试
else:
# 【选择放弃】
tb_str = '```\n' + trimmed_format_exc() + '```'
mutable[0] += f"[Local Message] 警告，在执行过程中遭遇问题, Traceback：\n\n{tb_str}\n\n"
return mutable[0] # 放弃
return mutable[0] # 放弃
except:
# 【第三种情况】：其他错误：重试几次
tb_str = '```\n' + trimmed_format_exc() + '```'
@ -107,14 +109,15 @@ def request_gpt_model_in_new_thread_with_ui_alive(
mutable[0] += f"[Local Message] 警告，在执行过程中遭遇问题, Traceback：\n\n{tb_str}\n\n"
if retry_op > 0:
retry_op -= 1
mutable[0] += f"[Local Message] 重试中，请稍等 {retry_times_at_unknown_error-retry_op}/{retry_times_at_unknown_error}：\n\n"
mutable[
0] += f"[Local Message] 重试中，请稍等 {retry_times_at_unknown_error - retry_op}/{retry_times_at_unknown_error}：\n\n"
if ("Rate limit reached" in tb_str) or ("Too Many Requests" in tb_str):
time.sleep(30)
time.sleep(5)
continue # 返回重试
continue # 返回重试
else:
time.sleep(5)
return mutable[0] # 放弃
return mutable[0] # 放弃
# 提交任务
future = executor.submit(_req_gpt, inputs, history, sys_prompt)
@ -126,21 +129,21 @@ def request_gpt_model_in_new_thread_with_ui_alive(
if future.done():
break
chatbot[-1] = [chatbot[-1][0], mutable[0]]
yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
final_result = future.result()
chatbot[-1] = [chatbot[-1][0], final_result]
yield from update_ui(chatbot=chatbot, history=[]) # 如果最后成功了，则删除报错信息
yield from update_ui(chatbot=chatbot, history=[]) # 如果最后成功了，则删除报错信息
return final_result
def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
inputs_array, inputs_show_user_array, llm_kwargs,
chatbot, history_array, sys_prompt_array,
inputs_array, inputs_show_user_array, llm_kwargs,
chatbot, history_array, sys_prompt_array,
refresh_interval=0.2, max_workers=-1, scroller_max_len=30,
handle_token_exceed=True, show_user_at_complete=False,
retry_times_at_unknown_error=2,
):
):
"""
Request GPT model using multiple threads with UI and high efficiency
请求GPT模型的[多线程]版。
@ -173,19 +176,21 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
from request_llm.bridge_all import predict_no_ui_long_connection
assert len(inputs_array) == len(history_array)
assert len(inputs_array) == len(sys_prompt_array)
if max_workers == -1: # 读取配置文件
try: max_workers, = get_conf('DEFAULT_WORKER_NUM')
except: max_workers = 8
if max_workers == -1: # 读取配置文件
try:
max_workers, = get_conf('DEFAULT_WORKER_NUM')
except:
max_workers = 8
if max_workers <= 0: max_workers = 3
# 屏蔽掉 chatglm的多线程，可能会导致严重卡顿
if not (llm_kwargs['llm_model'].startswith('gpt-') or llm_kwargs['llm_model'].startswith('api2d-')):
max_workers = 1
executor = ThreadPoolExecutor(max_workers=max_workers)
n_frag = len(inputs_array)
# 用户反馈
chatbot.append(["请开始多线程操作。", ""])
yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
# 跨线程传递
mutable = [["", time.time(), "等待中"] for _ in range(n_frag)]
@ -197,13 +202,13 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
mutable[index][2] = "执行中"
while True:
# watchdog error
if len(mutable[index]) >= 2 and (time.time()-mutable[index][1]) > 5:
if len(mutable[index]) >= 2 and (time.time() - mutable[index][1]) > 5:
raise RuntimeError("检测到程序终止。")
try:
# 【第一种情况】：顺利完成
# time.sleep(10); raise RuntimeError("测试")
gpt_say = predict_no_ui_long_connection(
inputs=inputs, llm_kwargs=llm_kwargs, history=history,
inputs=inputs, llm_kwargs=llm_kwargs, history=history,
sys_prompt=sys_prompt, observe_window=mutable[index], console_slience=True
)
mutable[index][2] = "已成功"
@ -217,10 +222,10 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
p_ratio, n_exceed = get_reduce_token_percent(str(token_exceeded_error))
MAX_TOKEN = 4096
EXCEED_ALLO = 512 + 512 * exceeded_cnt
inputs, history = input_clipping(inputs, history, max_token_limit=MAX_TOKEN-EXCEED_ALLO)
inputs, history = input_clipping(inputs, history, max_token_limit=MAX_TOKEN - EXCEED_ALLO)
gpt_say += f'[Local Message] 警告，文本过长将进行截断，Token溢出数：{n_exceed}。\n\n'
mutable[index][2] = f"截断重试"
continue # 返回重试
continue # 返回重试
else:
# 【选择放弃】
tb_str = '```\n' + trimmed_format_exc() + '```'
@ -236,7 +241,7 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
gpt_say += f"[Local Message] 警告，线程{index}在执行过程中遭遇问题, Traceback：\n\n{tb_str}\n\n"
if len(mutable[index][0]) > 0:
gpt_say += "此线程失败前收到的回答：\n\n" + mutable[index][0]
if retry_op > 0:
if retry_op > 0:
retry_op -= 1
wait = random.randint(5, 20)
if ("Rate limit reached" in tb_str) or ("Too Many Requests" in tb_str):
@ -246,19 +251,22 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
fail_info = ""
# 也许等待十几秒后，情况会好转
for i in range(wait):
mutable[index][2] = f"{fail_info}等待重试 {wait-i}"; time.sleep(1)
mutable[index][2] = f"{fail_info}等待重试 {wait - i}";
time.sleep(1)
# 开始重试
mutable[index][2] = f"重试中 {retry_times_at_unknown_error-retry_op}/{retry_times_at_unknown_error}"
mutable[index][
2] = f"重试中 {retry_times_at_unknown_error - retry_op}/{retry_times_at_unknown_error}"
continue # 返回重试
else:
mutable[index][2] = "已失败"
wait = 5
time.sleep(5)
return gpt_say # 放弃
return gpt_say # 放弃
# 异步任务开始
futures = [executor.submit(_req_gpt, index, inputs, history, sys_prompt) for index, inputs, history, sys_prompt in zip(
range(len(inputs_array)), inputs_array, history_array, sys_prompt_array)]
futures = [executor.submit(_req_gpt, index, inputs, history, sys_prompt) for index, inputs, history, sys_prompt in
zip(
range(len(inputs_array)), inputs_array, history_array, sys_prompt_array)]
cnt = 0
while True:
# yield一次以刷新前端页面
@ -272,17 +280,17 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
mutable[thread_index][1] = time.time()
# 在前端打印些好玩的东西
for thread_index, _ in enumerate(worker_done):
print_something_really_funny = "[ ...`"+mutable[thread_index][0][-scroller_max_len:].\
print_something_really_funny = "[ ...`" + mutable[thread_index][0][-scroller_max_len:]. \
replace('\n', '').replace('```', '...').replace(
' ', '.').replace('<br/>', '.....').replace('$', '.')+"`... ]"
' ', '.').replace('<br/>', '.....').replace('$', '.') + "`... ]"
observe_win.append(print_something_really_funny)
# 在前端打印些好玩的东西
stat_str = ''.join([f'`{mutable[thread_index][2]}`: {obs}\n\n'
if not done else f'`{mutable[thread_index][2]}`\n\n'
stat_str = ''.join([f'`{mutable[thread_index][2]}`: {obs}\n\n'
if not done else f'`{mutable[thread_index][2]}`\n\n'
for thread_index, done, obs in zip(range(len(worker_done)), worker_done, observe_win)])
# 在前端打印些好玩的东西
chatbot[-1] = [chatbot[-1][0], f'多线程操作已经开始，完成情况: \n\n{stat_str}' + ''.join(['.']*(cnt % 10+1))]
yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
chatbot[-1] = [chatbot[-1][0], f'多线程操作已经开始，完成情况: \n\n{stat_str}' + ''.join(['.'] * (cnt % 10 + 1))]
yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
if all(worker_done):
executor.shutdown()
break
@ -292,13 +300,13 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
for inputs_show_user, f in zip(inputs_show_user_array, futures):
gpt_res = f.result()
gpt_response_collection.extend([inputs_show_user, gpt_res])
# 是否在结束时，在界面上显示结果
if show_user_at_complete:
for inputs_show_user, f in zip(inputs_show_user_array, futures):
gpt_res = f.result()
chatbot.append([inputs_show_user, gpt_res])
yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
time.sleep(0.3)
return gpt_response_collection
@ -311,6 +319,7 @@ def breakdown_txt_to_satisfy_token_limit(txt, get_token_fn, limit):
lines = txt_tocut.split('\n')
estimated_line_cut = limit / get_token_fn(txt_tocut) * len(lines)
estimated_line_cut = int(estimated_line_cut)
cnt = 0
for cnt in reversed(range(estimated_line_cut)):
if must_break_at_empty_line:
if lines[cnt] != "":
@ -327,6 +336,7 @@ def breakdown_txt_to_satisfy_token_limit(txt, get_token_fn, limit):
result = [prev]
result.extend(cut(post, must_break_at_empty_line))
return result
try:
return cut(txt, must_break_at_empty_line=True)
except RuntimeError:
@ -342,9 +352,10 @@ def force_breakdown(txt, limit, get_token_fn):
return txt[:i], txt[i:]
return "Tiktoken未知错误", "Tiktoken未知错误"
def breakdown_txt_to_satisfy_token_limit_for_pdf(txt, get_token_fn, limit):
# 递归
def cut(txt_tocut, must_break_at_empty_line, break_anyway=False):
def cut(txt_tocut, must_break_at_empty_line, break_anyway=False):
if get_token_fn(txt_tocut) <= limit:
return [txt_tocut]
else:
@ -370,6 +381,7 @@ def breakdown_txt_to_satisfy_token_limit_for_pdf(txt, get_token_fn, limit):
result = [prev]
result.extend(cut(post, must_break_at_empty_line, break_anyway=break_anyway))
return result
try:
# 第1次尝试，将双空行（\n\n）作为切分点
return cut(txt, must_break_at_empty_line=True)
@ -380,7 +392,7 @@ def breakdown_txt_to_satisfy_token_limit_for_pdf(txt, get_token_fn, limit):
except RuntimeError:
try:
# 第3次尝试，将英文句号（.）作为切分点
res = cut(txt.replace('.', '。\n'), must_break_at_empty_line=False) # 这个中文的句号是故意的，作为一个标识而存在
res = cut(txt.replace('.', '。\n'), must_break_at_empty_line=False) # 这个中文的句号是故意的，作为一个标识而存在
return [r.replace('。\n', '.') for r in res]
except RuntimeError as e:
try:
@ -392,7 +404,6 @@ def breakdown_txt_to_satisfy_token_limit_for_pdf(txt, get_token_fn, limit):
return cut(txt, must_break_at_empty_line=False, break_anyway=True)
def read_and_clean_pdf_text(fp):
"""
这个函数用于分割pdf，用了很多trick，逻辑较乱，效果奇好
@ -420,8 +431,9 @@ def read_and_clean_pdf_text(fp):
fc = 0 # Index 0 文本
fs = 1 # Index 1 字体
fb = 2 # Index 2 框框
REMOVE_FOOT_NOTE = True # 是否丢弃掉 不是正文的内容 （比正文字体小，如参考文献、脚注、图注等）
REMOVE_FOOT_FFSIZE_PERCENT = 0.95 # 小于正文的？时，判定为不是正文（有些文章的正文部分字体大小不是100%统一的，有肉眼不可见的小变化）
REMOVE_FOOT_NOTE = True # 是否丢弃掉 不是正文的内容 （比正文字体小，如参考文献、脚注、图注等）
REMOVE_FOOT_FFSIZE_PERCENT = 0.95 # 小于正文的？时，判定为不是正文（有些文章的正文部分字体大小不是100%统一的，有肉眼不可见的小变化）
def primary_ffsize(l):
"""
提取文本块主字体
@ -431,12 +443,12 @@ def read_and_clean_pdf_text(fp):
if wtf['size'] not in fsize_statiscs: fsize_statiscs[wtf['size']] = 0
fsize_statiscs[wtf['size']] += len(wtf['text'])
return max(fsize_statiscs, key=fsize_statiscs.get)
def ffsize_same(a,b):
def ffsize_same(a, b):
"""
提取字体大小是否近似相等
"""
return abs((a-b)/max(a,b)) < 0.02
return abs((a - b) / max(a, b)) < 0.02
with fitz.open(fp) as doc:
meta_txt = []
@ -456,18 +468,19 @@ def read_and_clean_pdf_text(fp):
if len(txt_line) == 0: continue
pf = primary_ffsize(l)
meta_line.append([txt_line, pf, l['bbox'], l])
for wtf in l['spans']: # for l in t['lines']:
for wtf in l['spans']: # for l in t['lines']:
meta_span.append([wtf['text'], wtf['size'], len(wtf['text'])])
# meta_line.append(["NEW_BLOCK", pf])
# 块元提取 for each word segment with in line for each line cross-line words for each block
# 块元提取 for each word segment with in line for each line
# cross-line words for each block
meta_txt.extend([" ".join(["".join([wtf['text'] for wtf in l['spans']]) for l in t['lines']]).replace(
'- ', '') for t in text_areas['blocks'] if 'lines' in t])
meta_font.extend([np.mean([np.mean([wtf['size'] for wtf in l['spans']])
for l in t['lines']]) for t in text_areas['blocks'] if 'lines' in t])
for l in t['lines']]) for t in text_areas['blocks'] if 'lines' in t])
if index == 0:
page_one_meta = [" ".join(["".join([wtf['text'] for wtf in l['spans']]) for l in t['lines']]).replace(
'- ', '') for t in text_areas['blocks'] if 'lines' in t]
############################## <第 2 步，获取正文主字体> ##################################
fsize_statiscs = {}
for span in meta_span:
@ -481,32 +494,33 @@ def read_and_clean_pdf_text(fp):
mega_sec = []
sec = []
for index, line in enumerate(meta_line):
if index == 0:
if index == 0:
sec.append(line[fc])
continue
if REMOVE_FOOT_NOTE:
if meta_line[index][fs] <= give_up_fize_threshold:
continue
if ffsize_same(meta_line[index][fs], meta_line[index-1][fs]):
if ffsize_same(meta_line[index][fs], meta_line[index - 1][fs]):
# 尝试识别段落
if meta_line[index][fc].endswith('.') and\
(meta_line[index-1][fc] != 'NEW_BLOCK') and \
(meta_line[index][fb][2] - meta_line[index][fb][0]) < (meta_line[index-1][fb][2] - meta_line[index-1][fb][0]) * 0.7:
if meta_line[index][fc].endswith('.') and \
(meta_line[index - 1][fc] != 'NEW_BLOCK') and \
(meta_line[index][fb][2] - meta_line[index][fb][0]) < (
meta_line[index - 1][fb][2] - meta_line[index - 1][fb][0]) * 0.7:
sec[-1] += line[fc]
sec[-1] += "\n\n"
else:
sec[-1] += " "
sec[-1] += line[fc]
else:
if (index+1 < len(meta_line)) and \
meta_line[index][fs] > main_fsize:
if (index + 1 < len(meta_line)) and \
meta_line[index][fs] > main_fsize:
# 单行 + 字体大
mega_sec.append(copy.deepcopy(sec))
sec = []
sec.append("# " + line[fc])
else:
# 尝试识别section
if meta_line[index-1][fs] > meta_line[index][fs]:
if meta_line[index - 1][fs] > meta_line[index][fs]:
sec.append("\n" + line[fc])
else:
sec.append(line[fc])
@ -525,13 +539,15 @@ def read_and_clean_pdf_text(fp):
if len(block_txt) < 100:
meta_txt[index] = '\n'
return meta_txt
meta_txt = 把字符太少的块清除为回车(meta_txt)
def 清理多余的空行(meta_txt):
for index in reversed(range(1, len(meta_txt))):
if meta_txt[index] == '\n' and meta_txt[index-1] == '\n':
if meta_txt[index] == '\n' and meta_txt[index - 1] == '\n':
meta_txt.pop(index)
return meta_txt
meta_txt = 清理多余的空行(meta_txt)
def 合并小写开头的段落块(meta_txt):
@ -542,16 +558,18 @@ def read_and_clean_pdf_text(fp):
return True
else:
return False
for _ in range(100):
for index, block_txt in enumerate(meta_txt):
if starts_with_lowercase_word(block_txt):
if meta_txt[index-1] != '\n':
meta_txt[index-1] += ' '
if meta_txt[index - 1] != '\n':
meta_txt[index - 1] += ' '
else:
meta_txt[index-1] = ''
meta_txt[index-1] += meta_txt[index]
meta_txt[index - 1] = ''
meta_txt[index - 1] += meta_txt[index]
meta_txt[index] = '\n'
return meta_txt
meta_txt = 合并小写开头的段落块(meta_txt)
meta_txt = 清理多余的空行(meta_txt)
@ -571,7 +589,7 @@ def read_and_clean_pdf_text(fp):
return meta_txt, page_one_meta
def get_files_from_everything(txt, type): # type='.md'
def get_files_from_everything(txt, type): # type='.md'
"""
这个函数是用来获取指定目录下所有指定类型（如.md）的文件，并且对于网络上的文件，也可以获取它。
下面是对每个参数和返回值的说明：
@ -593,9 +611,10 @@ def get_files_from_everything(txt, type): # type='.md'
from toolbox import get_conf
proxies, = get_conf('proxies')
r = requests.get(txt, proxies=proxies)
with open('./gpt_log/temp'+type, 'wb+') as f: f.write(r.content)
with open('./gpt_log/temp' + type, 'wb+') as f:
f.write(r.content)
project_folder = './gpt_log/'
file_manifest = ['./gpt_log/temp'+type]
file_manifest = ['./gpt_log/temp' + type]
elif txt.endswith(type):
# 直接给定文件
file_manifest = [txt]
@ -603,7 +622,7 @@ def get_files_from_everything(txt, type): # type='.md'
elif os.path.exists(txt):
# 本地路径，递归搜索
project_folder = txt
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*'+type, recursive=True)]
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*' + type, recursive=True)]
if len(file_manifest) == 0:
success = False
else:
@ -614,16 +633,14 @@ def get_files_from_everything(txt, type): # type='.md'
return success, file_manifest, project_folder
def Singleton(cls):
_instance = {}
def _singleton(*args, **kargs):
if cls not in _instance:
_instance[cls] = cls(*args, **kargs)
return _instance[cls]
return _singleton
@ -642,31 +659,30 @@ class knowledge_archive_interface():
from toolbox import ProxyNetworkActivate
print('Checking Text2vec ...')
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
with ProxyNetworkActivate(): # 临时地激活代理网络
with ProxyNetworkActivate(): # 临时地激活代理网络
self.text2vec_large_chinese = HuggingFaceEmbeddings(model_name="GanymedeNil/text2vec-large-chinese")
return self.text2vec_large_chinese
def feed_archive(self, file_manifest, id="default"):
self.threadLock.acquire()
# import uuid
self.current_id = id
from zh_langchain import construct_vector_store
self.qa_handle, self.kai_path = construct_vector_store(
vs_id=self.current_id,
files=file_manifest,
self.qa_handle, self.kai_path = construct_vector_store(
vs_id=self.current_id,
files=file_manifest,
sentence_size=100,
history=[],
one_conent="",
one_content_segmentation="",
text2vec = self.get_chinese_text2vec(),
text2vec=self.get_chinese_text2vec(),
)
self.threadLock.release()
def get_current_archive_id(self):
return self.current_id
def get_loaded_file(self):
return self.qa_handle.get_loaded_file()
@ -675,30 +691,31 @@ class knowledge_archive_interface():
if not self.current_id == id:
self.current_id = id
from zh_langchain import construct_vector_store
self.qa_handle, self.kai_path = construct_vector_store(
vs_id=self.current_id,
files=[],
self.qa_handle, self.kai_path = construct_vector_store(
vs_id=self.current_id,
files=[],
sentence_size=100,
history=[],
one_conent="",
one_content_segmentation="",
text2vec = self.get_chinese_text2vec(),
text2vec=self.get_chinese_text2vec(),
)
VECTOR_SEARCH_SCORE_THRESHOLD = 0
VECTOR_SEARCH_TOP_K = 4
CHUNK_SIZE = 512
resp, prompt = self.qa_handle.get_knowledge_based_conent_test(
query = txt,
vs_path = self.kai_path,
query=txt,
vs_path=self.kai_path,
score_threshold=VECTOR_SEARCH_SCORE_THRESHOLD,
vector_search_top_k=VECTOR_SEARCH_TOP_K,
vector_search_top_k=VECTOR_SEARCH_TOP_K,
chunk_conent=True,
chunk_size=CHUNK_SIZE,
text2vec = self.get_chinese_text2vec(),
text2vec=self.get_chinese_text2vec(),
)
self.threadLock.release()
return resp, prompt
def try_install_deps(deps):
for dep in deps:
import subprocess, sys

3
requirements.txt
View File

@ -16,4 +16,5 @@ openai
numpy
arxiv
rich
langchain
langchain
zh_langchain